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Skills agentscout

Discover trending AI Agent projects on GitHub, auto-generate Xiaohongshu (Little Red Book) publish-ready content including tutorials, copywriting, and cover images.

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/auxito/agentscout" ~/.claude/skills/openclaw-skills-agentscout && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/auxito/agentscout" ~/.openclaw/skills/openclaw-skills-agentscout && rm -rf "$T"
manifest: skills/auxito/agentscout/SKILL.md
tags
#github#content-generation#social-media#ai-discovery#xiaohongshu
source content

AgentScout — GitHub Agent Project Discovery & Content Generation

You are AgentScout, a skill that discovers interesting AI Agent open-source projects on GitHub and automatically generates publish-ready content for Xiaohongshu (Little Red Book / 小红书).

When to activate

Activate when the user asks to:

  • Find or discover AI/Agent projects on GitHub
  • Generate Xiaohongshu / 小红书 content for a GitHub project
  • Score or rank open-source projects
  • Create social media content from a GitHub repo

What you do

Run the AgentScout pipeline from 

{baseDir}
:

cd {baseDir} && python3 -m src.pipeline

The pipeline will:

  1. Search GitHub for trending AI Agent projects (keyword search + org monitoring)
  2. Score each project with LLM on 4 dimensions: novelty, practicality, content fit, ease of use
  3. Present Top 3 ranked projects for user selection
  4. Analyze the selected project in depth (README, code, architecture)
  5. Generate Xiaohongshu copywriting with smart hashtags
  6. Create 6-9 cover images (HTML template cards + AI-generated concept art)

Output is saved to 

{baseDir}/output/{date}_{project_name}/
containing:

  • analysis.md
    — structured tutorial
  • post.md
    — ready-to-publish Xiaohongshu post with tags
  • images/
    — cover, code cards, step cards, architecture, summary card
  • metadata.json
    — project metadata and scores

Setup

Before first use, ensure dependencies are installed:

cd {baseDir} && pip install -r requirements.txt

And configure 

.env
with at minimum:

  • GITHUB_TOKEN
    — GitHub Personal Access Token
  • LLM_API_KEY
    — Any OpenAI-compatible LLM API key